Cross‐scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins

Hattermann, Fred F.; Krysanova, Valentina; Gosling, Simon N.; Dankers, Rutger; Daggupati, Prasad; Donnelly, Chantal; Flörke, Martina; Huang, Shaochun; Motovilov, Yu. G.; Su, Buda; Yang, Tao; Müller, Christoph; Leng, Guoyong; Tang, Qiuhong; Portmann, Felix T.; Hagemann, Stefan; Gerten, Dieter; Wada, Yoshihide; Masaki, Yoshimitsu; Alemayehu, T.; Satoh, Yusuke; Samaniego, Luis

doi:10.1007/s10584-016-1829-4

Cited by 161 publications

(131 citation statements)

References 41 publications

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“…The ability of HMs to represent hydrological changes is an important issue that is often neglected in climate change impact assessments and how this should be done is not agreed upon in the scientific community (e.g. Hatterman et al 2016, Merz et al 2011. Refsgaard et al (2013) and Coron et al (2011) suggest how this could be done for catchment-scale impact modelling but these methods are difficult to implement in continental-or globalscale models.…”

Section: Uncertainties and Limitationsmentioning

confidence: 99%

Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level

et al. 2017

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Impacts of climate change at 1.5, 2 and 3°C mean global warming above preindustrial level are investigated and compared for runoff, discharge and snowpack in Europe. Ensembles of climate projections representing each of the warming levels were assembled to describe the hydro-meteorological climate at 1.5, 2 and 3°C. These ensembles were then used to force an ensemble of five hydrological models and changes to hydrological indicators were calculated. It is seen that there are clear changes in local impacts on evapotranspiration, mean, low and high runoff and snow water equivalent between a 1.5, 2 and 3°C degree warmer world. In a warmer world, the hydrological impacts of climate change are more intense and spatially more extensive. Robust increases in runoff affect the Scandinavian mountains at 1.5°C, but at 3°C extend over most of Norway, Sweden and northern Poland. At 3°C, Norway is affected by robust changes in all indicators. Decreases in mean annual runoff are seen only in Portugal at 1.5°C warming, but at 3°C warming, decreases to runoff are seen around the entire Iberian coast, the Balkan Coast and parts of the French coast. In affected parts of Europe, there is a distinct increase in the changes to mean, low and high runoff at 2°C compared to 1.5°C, strengthening the case for mitigation to lower levels of global warming. Between 2 and 3°C, the changes in low and high runoff levels continue to increase, but the Climatic Change (2017) 143: 13-26 DOI 10.100713-26 DOI 10. /s10584-017-1971 Electronic supplementary material The online version of this article (doi:10.1007/s10584-017-1971-7) contains supplementary material, which is available to authorized users.

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Section: Uncertainties and Limitationsmentioning

confidence: 99%

Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level

et al. 2017

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“…We excluded the Nile River from the investigation because its discharge was considerably overestimated. It is frequently reported that GHMs substantially overestimate the river discharge of the Nile River (e.g., Haddeland et al, 2011;Hattermann et al, 2017). This poor performance for the Nile River by H08 was attributed not only to the model's formulation but also to the reliability of meteorological data in the basin, which has been commonly seen in other GHMs.…”

Section: Validation At Selected Basinsmentioning

confidence: 98%

“…Although the key hydrological processes were represented, the land surface hydrology and river routing sub-models of H08 are relatively simple (Appendix A). Moreover, the hydrological parameters were not tuned to individual basins, which yielded a generally lower reproducibility of historical river flow observations (e.g., Hattermann et al, 2017). In cases in which the H08 model was applied to specific basins, sensitivity testing and hydrological parameter calibration were conducted systematically using reliable long-term observations (e.g., Hanasaki et al, 2014;Masood et al, 2015).…”

Section: Potential Sources Of Uncertaintymentioning

confidence: 99%

A global hydrological simulation to specify the sources of water used by humans

Hanasaki

Yoshikawa

Pokhrel

et al. 2018

Hydrol. Earth Syst. Sci.

225

179

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Abstract. Humans abstract water from various sources to sustain their livelihood and society. Some global hydrological models (GHMs) include explicit schemes of human water abstraction, but the representation and performance of these schemes remain limited. We substantially enhanced the water abstraction schemes of the H08 GHM. This enabled us to estimate water abstraction from six major water sources, namely, river flow regulated by global reservoirs (i.e., reservoirs regulating the flow of the world's major rivers), aqueduct water transfer, local reservoirs, seawater desalination, renewable groundwater, and nonrenewable groundwater. In its standard setup, the model covers the whole globe at a spatial resolution of 0.5• , and the calculation interval is 1 day. All the interactions were simulated in a single computer program, and all water fluxes and storage were strictly traceable at any place and time during the simulation period. A global hydrological simulation was conducted to validate the performance of the model for the period of 1979-2013 (land use was fixed for the year 2000). The simulated water fluxes for water abstraction were validated against those reported in earlier publications and showed a reasonable agreement at the global and country level. The simulated monthly river discharge and terrestrial water storage (TWS) for six of the world's most significantly human-affected river basins were compared with gauge observations and the data derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. It is found that the simulation including the newly added schemes outperformed the simulation without human activities. The simulated results indicated that, in 2000, of the 3628±75 km 3 yr −1 global freshwater requirement, 2839 ± 50 km 3 yr −1 was taken from surface water and 789 ± 30 km 3 yr −1 from groundwater. Streamflow, aqueduct water transfer, local reservoirs, and seawater desalination accounted for 1786 ± 23, 199 ± 10, 106 ± 5, and 1.8 ± 0 km 3 yr −1 of the surface water, respectively. The remaining 747 ± 45 km 3 yr −1 freshwater requirement was unmet, or surface water was not available when and where it was needed in our simulation. Renewable and nonrenewable groundwater accounted for 607 ± 11 and 182 ± 26 km 3 yr −1 of the groundwater total, respectively. Each source differed in its renewability, economic costs for development, and environmental consequences of usage. The model is useful for performing global water resource assessments by considering the aspects of sustainability, economy, and environment.

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“…In the context of the present study we are not able to identify the exact reasons why model performance is hindered in some basins. It is unrealistic for a global LSM to achieve top performance around the world (Hattermann et al, 2017), as, due to its global nature, some fixes in some regions could result in deteriorations in performance in other parts of the land surface. Thus, the interpretation of the following analysis of the present study should consider the model deficiencies revealed in this section.…”

Section: Model Evaluationmentioning

confidence: 99%

“…These variables have traditionally been prioritized for bias correction as they are considered the most important driving variables of hydrological processes in modelling applications -even though from a physical perspective radiation is the driving force of the hydrological cycle. However, many state-of-the-art regional and global hydrological models (GHMs) and land surface models (LSMs) require -apart from precipitation and temperature -additional meteorological forcing, such as solar radiation, air humidity, surface air pressure, and wind speed (a summary of the input variables needed by various hydrological models can be found in the Supplement of Hattermann et al, 2017). For this reason, biases in variables like radiation, humidity, and wind speed can hinder the representation of hydrological fluxes such as runoff, evapotranspiration (ET), snow accumulation, and snowmelt by the impact models (Hagemann et al, 2011;Haddeland et al, 2012), indicating that bias correction should be extended to include more input variables.…”

Section: Introductionmentioning

confidence: 99%

The effect of GCM biases on global runoff simulations of a land surface model

Papadimitriou

Koutroulis

Grillakis

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

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Cross‐scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins

Cited by 161 publications

References 41 publications

Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level

Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level

A global hydrological simulation to specify the sources of water used by humans

The effect of GCM biases on global runoff simulations of a land surface model

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